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Article

Permeable Breakwaters Performance Modeling: A Comparative Study of Machine Learning Techniques

1
Department of Civil Engineering, College of Engineering, University of Tehran, Tehran 1417613131, Iran
2
Department of Civil and Environmental Engineering, College of Engineering, Shiraz University, Shiraz 7134851156, Iran
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(11), 1856; https://doi.org/10.3390/rs12111856
Received: 27 March 2020 / Revised: 28 April 2020 / Accepted: 4 May 2020 / Published: 8 June 2020
(This article belongs to the Special Issue Data Mining and Machine Learning in Urban Applications)
The advantage of permeable breakwaters over more traditional types has attracted great interest in the behavior of these structures in the field of engineering. The main objective of this study is to apply 19 well-known machine learning regressors to derive the best model of innovative breakwater hydrodynamic behavior with reflection and transmission coefficients as the target parameters. A database of 360 laboratory tests on the low-scale breakwater is used to establish the model. The proposed models link the reflection and transmission coefficients to seven dimensionless parameters, including relative chamber width, relative rockfill height, relative chamber width in terms of wavelength, wave steepness, wave number multiplied by water depth, and relative wave height in terms of rockfill height. For the validation of the models, the cross-validation method was used for all models except the multilayer perceptron neural network (MLP) and genetic programming (GP) models. To validate the MLP and GP, the database is divided into three categories: training, validation, and testing. Furthermore, two explicit functional relationships are developed by utilizing the GP for each target. The exponential Gaussian process regression (GPR) model in predicting the reflection coefficient (R2 = 0.95, OBJ function = 0.0273), and similarly, the exponential GPR model in predicting the transmission coefficient (R2 = 0.98, OBJ function = 0.0267) showed the best performance and the highest correlation with the actual records and can further be used as a reference for engineers in practical work. Also, the sensitivity analysis of the proposed models determined that the relative height parameter of the rockfill material has the greatest contribution to the introduced breakwater behavior. View Full-Text
Keywords: permeable breakwater; machine learning regressors; reflection; transmission; sensitivity analysis permeable breakwater; machine learning regressors; reflection; transmission; sensitivity analysis
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MDPI and ACS Style

Gandomi, M.; Dolatshahi Pirooz, M.; Varjavand, I.; Nikoo, M.R. Permeable Breakwaters Performance Modeling: A Comparative Study of Machine Learning Techniques. Remote Sens. 2020, 12, 1856. https://doi.org/10.3390/rs12111856

AMA Style

Gandomi M, Dolatshahi Pirooz M, Varjavand I, Nikoo MR. Permeable Breakwaters Performance Modeling: A Comparative Study of Machine Learning Techniques. Remote Sensing. 2020; 12(11):1856. https://doi.org/10.3390/rs12111856

Chicago/Turabian Style

Gandomi, Mostafa, Moharram Dolatshahi Pirooz, Iman Varjavand, and Mohammad R. Nikoo. 2020. "Permeable Breakwaters Performance Modeling: A Comparative Study of Machine Learning Techniques" Remote Sensing 12, no. 11: 1856. https://doi.org/10.3390/rs12111856

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